In order to make big gains in machine learning for ecology we need big datasets which are able to be used with state of the art algorithms. Currently Global Archive contributors have put together a dataset which could underpin great advances in automation of fish species detection, but due to the current method of video analysis the resulting data is unsuitable for use in state of the art algorithms and needs to be heavily synthesized and standardised to suit current standards.
This project aims to transform an existing ARDC funded collection from Global Archive, AIMS and University of Western Australia, into a standardised data (image, video and labels) collection for the purposes of rapidly advancing machine learning research into environmental monitoring of Australian fish species.
Who is this project for?
- Machine Learning community
- Marine research sector
- Research organisations
- Government (state and commonwealth)
What does this project enable?
This project will ultimately enable advances in automated classification of fish from BRUV video and imagery in Australian waters. Initially we hope to plant a seed for other organisations and the BRUV community to continue to contribute data to an open dataset into the future.